mirror of
https://github.com/illiumst/marl-factory-grid.git
synced 2025-05-23 15:26:43 +02:00
327 lines
13 KiB
Python
327 lines
13 KiB
Python
import pickle
|
|
from pathlib import Path
|
|
from typing import List, Union, Iterable
|
|
|
|
import gym
|
|
import numpy as np
|
|
from gym import spaces
|
|
|
|
import yaml
|
|
|
|
from environments import helpers as h
|
|
|
|
|
|
class AgentState:
|
|
|
|
def __init__(self, i: int, action: int):
|
|
self.i = i
|
|
self.action = action
|
|
|
|
self.collision_vector = None
|
|
self.action_valid = None
|
|
self.pos = None
|
|
self.info = {}
|
|
|
|
@property
|
|
def collisions(self):
|
|
return np.argwhere(self.collision_vector != 0).flatten()
|
|
|
|
def update(self, **kwargs): # is this hacky?? o.0
|
|
for key, value in kwargs.items():
|
|
if hasattr(self, key):
|
|
self.__setattr__(key, value)
|
|
else:
|
|
raise AttributeError(f'"{key}" cannot be updated, this attr is not a part of {self.__class__.__name__}')
|
|
|
|
|
|
class Register:
|
|
|
|
@property
|
|
def n(self):
|
|
return len(self)
|
|
|
|
def __init__(self):
|
|
self._register = dict()
|
|
|
|
def __len__(self):
|
|
return len(self._register)
|
|
|
|
def __add__(self, other: Union[str, List[str]]):
|
|
other = other if isinstance(other, list) else [other]
|
|
assert all([isinstance(x, str) for x in other]), f'All item names have to be of type {str}.'
|
|
self._register.update({key+len(self._register): value for key, value in enumerate(other)})
|
|
return self
|
|
|
|
def register_additional_items(self, other: Union[str, List[str]]):
|
|
self_with_additional_items = self + other
|
|
return self_with_additional_items
|
|
|
|
def keys(self):
|
|
return self._register.keys()
|
|
|
|
def items(self):
|
|
return self._register.items()
|
|
|
|
def __getitem__(self, item):
|
|
return self._register[item]
|
|
|
|
def by_name(self, item):
|
|
return list(self._register.keys())[list(self._register.values()).index(item)]
|
|
|
|
def __repr__(self):
|
|
return f'{self.__class__.__name__}({self._register})'
|
|
|
|
|
|
class Actions(Register):
|
|
|
|
@property
|
|
def movement_actions(self):
|
|
return self._movement_actions
|
|
|
|
def __init__(self, allow_square_movement=False, allow_diagonal_movement=False, allow_no_op=False):
|
|
# FIXME: There is a bug in helpers because there actions are ints. and the order matters.
|
|
assert not(allow_square_movement is False and allow_diagonal_movement is True), "There is a bug in helpers!!!"
|
|
super(Actions, self).__init__()
|
|
self.allow_no_op = allow_no_op
|
|
self.allow_diagonal_movement = allow_diagonal_movement
|
|
self.allow_square_movement = allow_square_movement
|
|
if allow_square_movement:
|
|
self + ['north', 'east', 'south', 'west']
|
|
if allow_diagonal_movement:
|
|
self + ['north-east', 'south-east', 'south-west', 'north-west']
|
|
self._movement_actions = self._register.copy()
|
|
if self.allow_no_op:
|
|
self + 'no-op'
|
|
|
|
|
|
class StateSlice(Register):
|
|
|
|
def __init__(self, n_agents: int):
|
|
super(StateSlice, self).__init__()
|
|
offset = 1
|
|
self.register_additional_items(['level', *[f'agent#{i}' for i in range(offset, n_agents+offset)]])
|
|
|
|
|
|
class BaseFactory(gym.Env):
|
|
|
|
@property
|
|
def action_space(self):
|
|
return spaces.Discrete(self._actions.n)
|
|
|
|
@property
|
|
def observation_space(self):
|
|
if self.pomdp_radius:
|
|
agent_slice = self.n_agents if self.omit_agent_slice_in_obs else 0
|
|
return spaces.Box(low=0, high=1, shape=(self._state.shape[0] - agent_slice, self.pomdp_radius * 2 + 1,
|
|
self.pomdp_radius * 2 + 1), dtype=np.float32)
|
|
else:
|
|
space = spaces.Box(low=0, high=1, shape=self._state.shape, dtype=np.float32)
|
|
return space
|
|
|
|
@property
|
|
def movement_actions(self):
|
|
return self._actions.movement_actions
|
|
|
|
def __init__(self, level='simple', n_agents=1, max_steps=int(5e2), pomdp_radius: Union[None, int] = None,
|
|
allow_square_movement=True, allow_diagonal_movement=True, allow_no_op=True,
|
|
omit_agent_slice_in_obs=False, **kwargs):
|
|
self.allow_no_op = allow_no_op
|
|
self.allow_diagonal_movement = allow_diagonal_movement
|
|
self.allow_square_movement = allow_square_movement
|
|
self.n_agents = n_agents
|
|
self.max_steps = max_steps
|
|
self.pomdp_radius = pomdp_radius
|
|
self.omit_agent_slice_in_obs = omit_agent_slice_in_obs
|
|
|
|
self.done_at_collision = False
|
|
_actions = Actions(allow_square_movement=self.allow_square_movement,
|
|
allow_diagonal_movement=self.allow_diagonal_movement,
|
|
allow_no_op=allow_no_op)
|
|
self._actions = _actions + self.additional_actions
|
|
|
|
self._level = h.one_hot_level(
|
|
h.parse_level(Path(__file__).parent / h.LEVELS_DIR / f'{level}.txt')
|
|
)
|
|
self._state_slices = StateSlice(n_agents)
|
|
if 'additional_slices' in kwargs:
|
|
self._state_slices.register_additional_items(kwargs.get('additional_slices'))
|
|
self.reset()
|
|
|
|
@property
|
|
def additional_actions(self) -> Union[str, List[str]]:
|
|
"""
|
|
When heriting from this Base Class, you musst implement this methode!!!
|
|
Please return a dict with the given types -> {int: str}.
|
|
The int should start at 0.
|
|
|
|
:return: An Actions-object holding all actions with keys in range 0-n.
|
|
:rtype: Actions
|
|
"""
|
|
raise NotImplementedError('Please register additional actions ')
|
|
|
|
def reset(self) -> (np.ndarray, int, bool, dict):
|
|
self.steps = 0
|
|
self._agent_states = []
|
|
# Agent placement ...
|
|
agents = np.zeros((self.n_agents, *self._level.shape), dtype=np.int8)
|
|
floor_tiles = np.argwhere(self._level == h.IS_FREE_CELL)
|
|
# ... on random positions
|
|
np.random.shuffle(floor_tiles)
|
|
for i, (x, y) in enumerate(floor_tiles[:self.n_agents]):
|
|
agents[i, x, y] = h.IS_OCCUPIED_CELL
|
|
agent_state = AgentState(i, -1)
|
|
agent_state.update(pos=[x, y])
|
|
self._agent_states.append(agent_state)
|
|
# state.shape = level, agent 1,..., agent n,
|
|
self._state = np.concatenate((np.expand_dims(self._level, axis=0), agents), axis=0)
|
|
# Returns State
|
|
return None
|
|
|
|
def _return_state(self):
|
|
if self.pomdp_radius:
|
|
pos = self._agent_states[0].pos
|
|
# pos = [agent_state.pos for agent_state in self.agent_states]
|
|
# obs = [] ... list comprehension... pos per agent
|
|
x0, x1 = max(0, pos[0] - self.pomdp_radius), pos[0] + self.pomdp_radius + 1
|
|
y0, y1 = max(0, pos[1] - self.pomdp_radius), pos[1] + self.pomdp_radius + 1
|
|
obs = self._state[:, x0:x1, y0:y1]
|
|
if obs.shape[1] != self.pomdp_radius ** 2 + 1 or obs.shape[2] != self.pomdp_radius ** 2 + 1:
|
|
obs_padded = np.full((obs.shape[0], self.pomdp_radius ** 2 + 1, self.pomdp_radius ** 2 + 1), 1)
|
|
a_pos = np.argwhere(obs[h.AGENT_START_IDX] == h.IS_OCCUPIED_CELL)[0]
|
|
obs_padded[:,
|
|
abs(a_pos[0]-self.pomdp_radius):abs(a_pos[0]-self.pomdp_radius)+obs.shape[1],
|
|
abs(a_pos[1]-self.pomdp_radius):abs(a_pos[1]-self.pomdp_radius)+obs.shape[2]] = obs
|
|
obs = obs_padded
|
|
else:
|
|
assert not self.omit_agent_slice_in_obs
|
|
obs = self._state
|
|
if self.omit_agent_slice_in_obs:
|
|
if obs.shape != (3, 5, 5):
|
|
print('Shiiiiiit')
|
|
obs_new = obs[[key for key, val in self._state_slices.items() if 'agent' not in val]]
|
|
if obs_new.shape != self.observation_space.shape:
|
|
print('Shiiiiiit')
|
|
return obs_new
|
|
else:
|
|
return obs
|
|
|
|
def do_additional_actions(self, agent_i: int, action: int) -> ((int, int), bool):
|
|
raise NotImplementedError
|
|
|
|
def step(self, actions):
|
|
actions = [actions] if isinstance(actions, int) or np.isscalar(actions) else actions
|
|
assert isinstance(actions, Iterable), f'"actions" has to be in [{int, list}]'
|
|
self.steps += 1
|
|
done = False
|
|
|
|
# Move this in a seperate function?
|
|
agent_states = list()
|
|
for agent_i, action in enumerate(actions):
|
|
agent_i_state = AgentState(agent_i, action)
|
|
if self._is_moving_action(action):
|
|
pos, valid = self.move_or_colide(agent_i, action)
|
|
elif self._is_no_op(action):
|
|
pos, valid = self.agent_i_position(agent_i), True
|
|
else:
|
|
pos, valid = self.do_additional_actions(agent_i, action)
|
|
# Update state accordingly
|
|
agent_i_state.update(pos=pos, action_valid=valid)
|
|
agent_states.append(agent_i_state)
|
|
|
|
for i, collision_vec in enumerate(self.check_all_collisions(agent_states, self._state.shape[0])):
|
|
agent_states[i].update(collision_vector=collision_vec)
|
|
if self.done_at_collision and collision_vec.any():
|
|
done = True
|
|
|
|
self._agent_states = agent_states
|
|
reward, info = self.calculate_reward(agent_states)
|
|
|
|
if self.steps >= self.max_steps:
|
|
done = True
|
|
|
|
info.update(step_reward=reward, step=self.steps)
|
|
|
|
return None, reward, done, info
|
|
|
|
def _is_moving_action(self, action):
|
|
return action in self._actions.movement_actions
|
|
|
|
def _is_no_op(self, action):
|
|
return self._actions[action] == 'no-op'
|
|
|
|
def check_all_collisions(self, agent_states: List[AgentState], collisions: int) -> np.ndarray:
|
|
collision_vecs = np.zeros((len(agent_states), collisions)) # n_agents x n_slices
|
|
for agent_state in agent_states:
|
|
# Register only collisions of moving agents
|
|
if self._is_moving_action(agent_state.action):
|
|
collision_vecs[agent_state.i] = self.check_collisions(agent_state)
|
|
return collision_vecs
|
|
|
|
def check_collisions(self, agent_state: AgentState) -> np.ndarray:
|
|
pos_x, pos_y = agent_state.pos
|
|
# FixMe: We need to find a way to spare out some dimensions, eg. an info dimension etc... a[?,]
|
|
collisions_vec = self._state[:, pos_x, pos_y].copy() # "vertical fiber" at position of agent i
|
|
collisions_vec[h.AGENT_START_IDX + agent_state.i] = h.IS_FREE_CELL # no self-collisions
|
|
if agent_state.action_valid:
|
|
# ToDo: Place a function hook here
|
|
pass
|
|
else:
|
|
# Place a marker to indicate a collision with the level boundrys
|
|
collisions_vec[h.LEVEL_IDX] = h.IS_OCCUPIED_CELL
|
|
return collisions_vec
|
|
|
|
def do_move(self, agent_i: int, old_pos: (int, int), new_pos: (int, int)) -> None:
|
|
(x, y), (x_new, y_new) = old_pos, new_pos
|
|
self._state[agent_i + h.AGENT_START_IDX, x, y] = h.IS_FREE_CELL
|
|
self._state[agent_i + h.AGENT_START_IDX, x_new, y_new] = h.IS_OCCUPIED_CELL
|
|
|
|
def move_or_colide(self, agent_i: int, action: int) -> ((int, int), bool):
|
|
old_pos, new_pos, valid = h.check_agent_move(state=self._state,
|
|
dim=agent_i + h.AGENT_START_IDX,
|
|
action=action)
|
|
if valid:
|
|
# Does not collide width level boundaries
|
|
self.do_move(agent_i, old_pos, new_pos)
|
|
return new_pos, valid
|
|
else:
|
|
# Agent seems to be trying to collide in this step
|
|
return old_pos, valid
|
|
|
|
def agent_i_position(self, agent_i: int) -> (int, int):
|
|
positions = np.argwhere(self._state[h.AGENT_START_IDX + agent_i] == h.IS_OCCUPIED_CELL)
|
|
assert positions.shape[0] == 1
|
|
pos_x, pos_y = positions[0] # a.flatten()
|
|
return pos_x, pos_y
|
|
|
|
def free_cells(self, excluded_slices: Union[None, List[int], int] = None) -> np.array:
|
|
excluded_slices = excluded_slices or []
|
|
assert isinstance(excluded_slices, (int, list))
|
|
excluded_slices = excluded_slices if isinstance(excluded_slices, list) else [excluded_slices]
|
|
|
|
state = self._state
|
|
|
|
if excluded_slices:
|
|
# Todo: Is there a cleaner way?
|
|
inds = list(range(self._state.shape[0]))
|
|
excluded_slices = [inds[x] if x < 0 else x for x in excluded_slices]
|
|
state = self._state[[x for x in inds if x not in excluded_slices]]
|
|
|
|
free_cells = np.argwhere(state.sum(0) == h.IS_FREE_CELL)
|
|
np.random.shuffle(free_cells)
|
|
return free_cells
|
|
|
|
def calculate_reward(self, agent_states: List[AgentState]) -> (int, dict):
|
|
# Returns: Reward, Info
|
|
raise NotImplementedError
|
|
|
|
def render(self):
|
|
raise NotImplementedError
|
|
|
|
def save_params(self, filepath: Path):
|
|
d = {key: val for key, val in self.__dict__.items() if not key.startswith('_') and not key.startswith('__')}
|
|
filepath.parent.mkdir(parents=True, exist_ok=True)
|
|
|
|
with filepath.open('wb') as f:
|
|
# yaml.dump(d, f)
|
|
pickle.dump(d, f, protocol=pickle.HIGHEST_PROTOCOL)
|